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Found 17 papers, 12 papers with code
On the Robustness of Cross-Concentrated Sampling for Matrix Completion
Matrix completion is one of the crucial tools in modern data science research.
Towards Constituting Mathematical Structures for Learning to Optimize
Learning to Optimize (L2O), a technique that utilizes machine learning to learn an optimization algorithm automatically from data, has gained arising attention in recent years.
Robust Tensor CUR Decompositions: Rapid Low-Tucker-Rank Tensor Recovery with Sparse Corruption
We study the tensor robust principal component analysis (TRPCA) problem, a tensorial extension of matrix robust principal component analysis (RPCA), that aims to split the given tensor into an underlying low-rank component and a sparse outlier component.
Non-convex approaches for low-rank tensor completion under tubal sampling
Tensor completion is an important problem in modern data analysis.
Matrix Completion with Cross-Concentrated Sampling: Bridging Uniform Sampling and CUR Sampling
While uniform sampling has been widely studied in the matrix completion literature, CUR sampling approximates a low-rank matrix via row and column samples.
Riemannian CUR Decompositions for Robust Principal Component Analysis
This algorithm has the same computational complexity as Iterated Robust CUR, which is currently state-of-the-art, but is more robust to outliers.
Learned Robust PCA: A Scalable Deep Unfolding Approach for High-Dimensional Outlier Detection
Robust principal component analysis (RPCA) is a critical tool in modern machine learning, which detects outliers in the task of low-rank matrix reconstruction.
Curvature-Aware Derivative-Free Optimization
Zeroth-order methods have been gaining popularity due to the demands of large-scale machine learning applications, and the paper focuses on the selection of the step size $\alpha_k$ in these methods.
Fast Robust Tensor Principal Component Analysis via Fiber CUR Decomposition
We study the problem of tensor robust principal component analysis (TRPCA), which aims to separate an underlying low-multilinear-rank tensor and a sparse outlier tensor from their sum.
Mode-wise Tensor Decompositions: Multi-dimensional Generalizations of CUR Decompositions
Low rank tensor approximation is a fundamental tool in modern machine learning and data science.
A Zeroth-Order Block Coordinate Descent Algorithm for Huge-Scale Black-Box Optimization
We consider the zeroth-order optimization problem in the huge-scale setting, where the dimension of the problem is so large that performing even basic vector operations on the decision variables is infeasible.
Robust CUR Decomposition: Theory and Imaging Applications
Additionally, we consider hybrid randomized and deterministic sampling methods which produce a compact CUR decomposition of a given matrix, and apply this to video sequences to produce canonical frames thereof.
Rapid Robust Principal Component Analysis: CUR Accelerated Inexact Low Rank Estimation
Robust principal component analysis (RPCA) is a widely used tool for dimension reduction.
A One-bit, Comparison-Based Gradient Estimator
By treating the gradient as an unknown signal to be recovered, we show how one can use tools from one-bit compressed sensing to construct a robust and reliable estimator of the normalized gradient.
Zeroth-Order Regularized Optimization (ZORO): Approximately Sparse Gradients and Adaptive Sampling
We consider the problem of minimizing a high-dimensional objective function, which may include a regularization term, using (possibly noisy) evaluations of the function.
Accelerated Structured Alternating Projections for Robust Spectrally Sparse Signal Recovery
We study the robust recovery problem for the spectrally sparse signal under the fully observed setting, which is about recovering $\boldsymbol{x}$ and a sparse corruption vector $\boldsymbol{s}$ from their sum $\boldsymbol{z}=\boldsymbol{x}+\boldsymbol{s}$.
Accelerated Alternating Projections for Robust Principal Component Analysis
We study robust PCA for the fully observed setting, which is about separating a low rank matrix $\boldsymbol{L}$ and a sparse matrix $\boldsymbol{S}$ from their sum $\boldsymbol{D}=\boldsymbol{L}+\boldsymbol{S}$.
